Seal Souvik, Bitler Benjamin G, Ghosh Debashis
Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, USA.
Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA.
bioRxiv. 2023 Mar 30:2023.03.23.533980. doi: 10.1101/2023.03.23.533980.
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.
在高通量空间转录组学（ST）研究中，识别那些在组织中的表达水平随细胞/斑点的空间位置而协变的基因是非常有意义的。这类基因，也被称为空间可变基因（SVG），对于理解复杂组织的结构和功能特征的生物学机制可能至关重要。现有的检测SVG的方法要么存在巨大的计算需求，要么显著缺乏统计效力。我们提出了一种名为SMASH的非参数方法，该方法在上述两个问题之间取得了平衡。我们在不同的模拟场景中将SMASH与其他现有方法进行比较，证明了其卓越的统计效力和稳健性。我们将该方法应用于来自不同平台的四个ST数据集，揭示了有趣的生物学见解。
